Rapid Triggering Capability Using an Adaptive Overlay during FPGA Debug
Bibliographic record
Abstract
Field Programmable Gate Array (FPGA) technology is rapidly gaining traction in a wide range of applications. Nonetheless, FPGAs still require long design and debug cycles. To debug hardware circuits, trace-based instrumentation is inserted into the design that enables capturing data during the circuit execution into on-chip memories for later offline analysis. Since on-chip memories are limited, a trigger circuitry is used to only record data related to specific events during the execution. However, during debugging, a circuit recompilation is required on modifying these instruments. This can be very slow, reducing debug productivity. In this article, we propose a non-intrusive and rapid triggering solution with a tailored overlay fabric and mapping algorithm that seeks to enable fast debug iterations without performing a recompilation. This overlay is specialized for small combinational and sequential circuits with a single output; such circuits are typical of common trigger functions. We present an adaptive strategy to construct the overlay fabric using spare FPGA resources at compile time. At debug time, our proposed trigger mapping algorithms adapt to this specialized overlay to rapidly implement combinational and sequential trigger circuits. Our results show that the overlay fabric can be reconfigured to map different triggering scenarios in less than 40s instead of recompiling the circuit during debug iterations, increasing debug productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".